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Title: Generalizing semi-supervised and unsupervised learning for domain adaptation with very large scientific data

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1474449
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: DOE ASCR Scientific Machine Learning (SciML 2018) - Washington DC, District of Columbia, United States of America - 1/29/2018 3:00:00 PM-2/1/2018 3:00:00 PM
Country of Publication:
United States
Language:
English

Citation Formats

Lunga, Dalton D., Page, David Lon, and Hughes, David. Generalizing semi-supervised and unsupervised learning for domain adaptation with very large scientific data. United States: N. p., 2018. Web.
Lunga, Dalton D., Page, David Lon, & Hughes, David. Generalizing semi-supervised and unsupervised learning for domain adaptation with very large scientific data. United States.
Lunga, Dalton D., Page, David Lon, and Hughes, David. Thu . "Generalizing semi-supervised and unsupervised learning for domain adaptation with very large scientific data". United States. https://www.osti.gov/servlets/purl/1474449.
@article{osti_1474449,
title = {Generalizing semi-supervised and unsupervised learning for domain adaptation with very large scientific data},
author = {Lunga, Dalton D. and Page, David Lon and Hughes, David},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {3}
}

Conference:
Other availability
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